Making a Grammar Checker with Autocorrect Options Using NLP Tools

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language understanding (NLU), dialogue management, and natural language generation (NLG). We designed and implemented an HRI system that leverages models such as BERT for language understanding and GPT-3 for generating contextually appropriate responses. Our methodology involves integrating these NLP models with a robotics platform, ensuring real-time interaction capabilities while maintaining a high level of accuracy and context awareness. The system was evaluated through a series of user studies, measuring performance metrics such as accuracy, latency, and user satisfaction. Results indicate that our NLP-enhanced HRI system significantly improves the quality of interactions, demonstrating superior understanding and responsiveness compared to traditional systems. This paper discusses the implementation challenges, including computational constraints and ambiguity resolution, and provides insights into user feedback and system performance. Future work will focus on enhancing context management, exploring multimodal interaction, and addressing ethical considerations in deploying advanced HRI systems. Our findings underscore the potential of NLP to transform human-robot communication, paving the way for more intuitive and effective robotic assistants in various domains. Keywords: Human-Robot Interaction (HRI), Natural Language Processing (NLP), Conversational AI, Speech Recognition, Natural Language Understanding (NLU), Natural Language Generation (NLG), Multimodal Interaction, Dialogue Systems, Context Awareness, Emotion Recognition, Machine Learning in HRI, Personalized Interaction, User Experience (UX) in HRI, Human-Centered Design, Collaborative Robots (Cobots)

From context-free to definite-clause grammars: a type-theoretic approach

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

Natural Language Processing, one of the most important branches of Artificial Intelligence, has close links with Logic Programming. It is one of the two roots from which Logic Programming developed, and still shares important core concepts with it (e.g. unification). The concepts and techniques used for two of its main tasks, viz. syntax analysis and semantic analysis, are illustrated with concrete examples. For syntax analysis, it is the notations of Definite Clause Grammars and Extraposition Grammars, for semantic analysis, the concepts of Montague Grammar and Generalized Quantifier Theory. Some of these techniques can also be applied to the analysis of formal languages.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented. RECENT ADVANCES in natural language processing [NLP] have generated considerable interest within the Artificial Intelligence [AI] and Cognitive Science communities. Within NLP, researchers are trying to produce intelligent computer systems that can read, understand, and respond to various human-oriented texts. Terrorism stories, airline flight schedules, and how to fill ice cube trays are all domains that have been used for NLP programs. In order to understand these texts and others, some way of representing information is needed. A complete understanding of human-oriented prose requires the ability to combine the meanings of many readings in an intelligent manner. Learning through the process of generalization is one such mechanism. The integration of representation and generalization in the domain of NLP is the subject of this article. Physical object understanding is an area in which a variety of representation schemes and generalization methods have been used. In past years, researchers have devised various representation systems for objects that range from very simple

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Natural Language (NL) is an essential part of ourlife. Humans use language for communication. NL is a prevailing tool used by the humans to convey the information. Natural Language Understanding (NLU) is a major challenge in Natural Language Processing (NLP). NLP is a part of Artificial Intelligence (AI). NLP provides a significant tool for communication. It attempts to produces noise free data and conversion of noise to text. NLU is having different levels. This paper presents the issue with respect to one of the level such as syntax analysis. To provide a solution for syntax analysis, dynamic fuzzy parser is designed and implemented to parse the English input sentences. Traditional approach of parsing is enhanced by applying fuzzy logic. This helps to know the syntactic correctness of the sentence. Penns tree bank parts of speech tags are used for the Parts of Speech Tagger (POS). POS tagger assigns the parts of speech tags for the input English sentence. Then these tags of the words are parsed using the grammar rules. Finally the result is displayed to represent the number of words parsed in a sentence with its associated fuzzy membership value. This parser produces Precision value of 1(100%), Recall value of 0.92 (92%) and F-measure value of 0.9583 for the sample of 50 correct and 50 incorrect sentences.

: If we want to build intelligent information retrieval systems, we will have to give them the capabilities of understanding Natural language, automatically organizing and reorganizing their memories, and using intelligent heuristics for searching their memories. These systems will have to analyze and understand both new text and Natural Language queries. In answering questions, they will have to direct memory search to reasonable places. This requires good organization of both the conceptual content of text and knowledge necessary for understanding those texts and accessing memory. The CYRUS and FRUMP systems (Kolodner (1978), Schank and Kolodner (1979), Dejong (1979)) comprise an information retrieval system called CyFr. Together, they have the analysis and retrieval capabilities mentioned above. FRUMP analysis news stories from the UPI wire for their conceptual content, and produces summaries of those stories. It sends summaries of stories about important people to CYRUS automatically adds those stories to its memory, and can then retrieve that information to answer question posed to it in natural language. This paper describes the problems involved in building such an intelligent system. It proposes solutions to some of those problems bases on recent research in Artificial Intelligence and Natural Language processing, and describes the CyFr system, which implements those solutions. The solutions we propose and implement are based on a model of human understanding and memory retrieval. (Author)

To understand the factors influencing AI engine robot use, this study developed a conceptual model along the lines of previous research. The research model hypothesized that personal innovativeness in Information Technology (new software, new hardware, new AI engine, new chatbot, use new technology), perceived usefulness of Information Technology and perceived ease of use of Information Technology positively affect attitude toward AI engine robots, which in turn facilitates AI engine robot intention and AI engine robot use. By collecting data from 55 surveys from college Student’s respondents who have employed AI engine robots, we applied statistics to test the relationships in the model. Our findings demonstrate a positive effect of personal innovativeness in Information Technology, perceived usefulness of Information Technology and perceived ease of use of Information Technology on attitude toward AI engine robots. In addition, attitude toward AI engine robots has a positive effect on AI engine robot intention and AI engine robot use. Students use the AI engine to score the natural language description of the copywriting. Future research in the field of natural language processing and natural language understanding, using several AI engines for mathematical operations and applications. Quantitative research on natural language processing and natural language understanding. Used to solve more complex NLP and NLU and sentiment analysis problems.

Case or agr. Just as abstract Case and agr are cover terms for syntactic features with often idiosyncratic morphological realization, thematic roles seem most useful simply as syntactic designations for positions that enter into argument and adjunct relations. This is a good book, not just for its critique of theta theory, but also for the positive contribution of further development of the Decompositional Theory. Although the book is a revised dissertation, it doesn't read like one: the style is engaging and philosophical, and the exemplification is detailed and clear. And while it does not cover every thematic-role-based proposal ever made, the book is nevertheless to be recommended to anyone who has puzzled over the content of thematic roles. [Edwin Battistella, University of Alabama in Birmingham] Psychology of language: An introduction to sentence and discourse processes. By Murray Singer. Hillsdale, NJ: Lawrence Erlbaum, 1990. Pages xi, 308. $24.95. Recent years have seen the emergence of an essentially interdisciplinary, cognitive-science approach to natural language processing, with some of the most fruitful work focusing on discourse structure and processing. Singer's book is intended as an introductory graduate textbook, though it is also suitable as a desk reference for the linguist, cognitive psychologist, or computer scientist with an interest in natural language processing. An introductory graduate textbook must accomplish several purposes: (i) familiarize students with key theoretical concepts; (ii) refer them to the fundamental literature in which those concepts are discussed; (iii) provide them with exemplars that teach them how to apply the concepts insightfully to the data; and (iv) inculcate a sense of relevance that enables students to read professional articles and evaluate their theoretical significance. S's book is probably above average by these standards. Key concepts are clearly if succinctly explained, fundamental articles are cited in each of the relevant disciplines, and examples are frequent and detailed enough to satisfy the most exacting linguist. The book's weaknesses are those endemic to any introductory text: it emphasizes consensus, spending more time summarizing established results than exploring open issues and controversies, and the passages which explain key concepts are often terse, so that the instructor may sometimes wish to assign source articles as background reading. Overall, the book appears to be a valuable introductory textbook for a course in psycholinguistics or discourse processing. It is divided into ten chapters, as follows: Ch. 1, 'Introduction'; Ch. 2, 'Language and meaning: Representing and remembering discourse'; Ch. 3, 'Syntax and parsing processes'; Ch. 4, The role of knowledge in language comprehension'; Ch. 5, 'Understanding coherent discourse'; Ch. 6, Theme'; Ch. 7, 'Inference processes'; Ch. 8, 'Understanding stories'; Ch. 9, 'Question answering and sentence verification'; and Ch. 10, 'Natural language understanding by computers—and people'. One of the noteworthy aspects of the book is its thorough integration of psycholinguistic data with the concepts of discourse analysis, including coherence, the givennew contract, thematic structure, and scripts. [Paul Deane, University of Central Florida.] An essay on grammar-parser relations. By Jan van de Koot. Dordrecht: Foris, 1990. Pp. xii, 152. Paper $24.50. In this monograph, K investigates the possible relations between grammars and parsers. Any theory of the mind that postulates separate 240 LANGUAGE, VOLUME 68, NUMBER 1 (1992) levels of representations raises the question of what relations are possible, justified and beneficial among the several levels. Two such theories are Marr's theory of vision and Chomskyan generative grammar, with their separation of competence from performance. The book under review belongs to the research line of computer modelling of performance started by Parsifal (Mitchell Marcus, A theory of syntactic recognition for natural language, Cambridge, MA: MIT Press, 1980) in the Government-Binding framework. The monograph starts with a critical introduction to the relevant issues. Through extensive quotations from Marr's work (David Marr, Vision, San Francisco, CA: Freeman, 1982) K presents the methodological justification for multileveled theories of cognition. He then proceeds to explore, in the more limited domain of the language faculty, the necessary features of a psychologically real theory of grammar. The arguments that led some scholars to consider transformational grammar not psychologically real are presented and rebutted by the introduction of a more articulated framework for evaluation, which takes into account the separateness of competence grammar, runtime grammar, parsing technique, and evaluation metrics. Although nothing new is added to the debate here, the main issues of a discussion that spans a decade are reviewed clearly and concisely. Ch. 2 is an overview of several proposals in the framework of deterministic parsing. Most notably, the first such parser, Parsifal, is presented in detail. Arguing against some recent criticism of Parsifal's use of look-ahead, K claims that the use of look-ahead to resolve local ambiguities is empirically correct if coupled with the assumption that lexical-semantic information can be used during syntactic processing, when it is needed for disambiguation. K also reviews the formal properties of Parsifal, drawing on other works in the literature to argue that determinism provides a functional explanation of WH-islands at most, and that it is neutral with respect to grammatical formalism. Ch. 3 explores the relation between the Barriers theory (Noam Chomsky, Barriers. Cambridge, MA: MIT Press, 1986) and deterministic parsing. The parsing platform is Parsifal, with alterations and augmentations that link it more tightly to the grammar and make it more accurate empirically. An algorithm for chain formation is proposed here. K discusses its integration in the existing parser and its application to some linguistic facts, such as violations of the Subject and Adjunct Conditions and parasitic gaps. Some interesting ideas are put forth in this chapter, although more extensive exemplification and, if not a real implementation, at least lower-level specifications of the algorithms would have substantiated the proposal more convincingly. The book ends with a number of theoretical investigations of the possible contribution of the theory of performance to the theory of competence. In general, the reader interested in these issues will find in K's book an accurate and critical review of the main issues in the tradition of deterministic parsing, in itself a needed and useful contribution. Because the book is not intended as a review, however, more space should have been devoted to the author's new proposal; interesting ideas, such as those related to the algorithms for chain formation, are not given enough space and detailed description. The book would have also benefited from more careful editing and typesetting. [Paola Merlo. University ofMaryland.] Politically speaking: The pragmatic analysis of political language. By John Wilson. (Language in Society series.) Basil Blackwell, 1990. Pp. 203. Cloth $45.95, paper $16.95. Politically speaking offers an interesting and frequently amusing look at political speech from the perspective of linguistic pragmatics. Unlike Robin Lakoffs Talking power (New York: Basic Books, 1990), it does not investigate political power or even politics in a generic sense; and, unlike Frederick Bowers's Linguistic aspects oflegislative expression (Vancouver: University of British Columbia Press, 1990). it does not examine statutory language. Rather, Wilson explores politicians' utterances in speeches and interviews. Examples from other cultures are used, but mostly the study deals with recent British and American political figures. A notable feature of the book, W's quotations—from Margaret Thatcher's 'We are a grandmother' to Ronald Reagan's '[in England] a criminal with a gun, even if he was arrested for burglary, was tried for first degree murder and hanged if found

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

As with Amazon’s Echo and its conversational agent Alexa, smart voice-controlled devices become ever more present in daily life, and many different applications can be integrated into this platform. In this paper, we present a framework that eases the development of skills in Prolog. As Prolog has a long history in natural language processing, we may integrate well-established techniques, such as reasoning about knowledge with Attempto Controlled English, instead of depending on example phrases and pre-defined slots.

Large Language Models (LLMs) have been the cutting-edge technology in natural language processing (NLP) in recent years, making machine-generated text indistinguishable from human-generated text. On the other hand, “rule-based” Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms were developed in earlier years, and they have performed well in certain areas of Natural Language Processing (NLP). Today, an arduous task that arises is how to estimate the quality of the produced text. This process depends on the aspects of text that you need to assess, varying from correct grammar and syntax to more intriguing aspects such as coherence and semantical fluency. Although the performance of LLMs is high, the challenge is whether LLMs can cooperate with rule-based NLG/NLU technology by leveraging their assets to overcome LLMs’ weak points. This paper presents the basics of these two families of technologies and the applications, strengths, and weaknesses of each approach, analyzes the different ways of evaluating a machine-generated text, and, lastly, focuses on a first-level approach of possible combinations of these two approaches to enhance performance in specific tasks.

Semantic representation is the task of conveying the meaning of a natural language utterance by converting it to a logical form that can be processed and understood by machines. It is utilised by many applications in natural language processing (NLP), particularly in tasks relevant to natural language understanding (NLU). Due to the widespread use of semantic parsing in NLP, many semantic representation schemes with different forms have been proposed; Universal Conceptual Cognitive Annotation (UCCA) is one of them. UCCA is a cross-lingual semantic annotation framework that allows easy annotation without requiring substantial linguistic knowledge. UCCA-annotated datasets have been released so far for English, French, German, Russian, and Hebrew. In this paper, we present a UCCA-annotated Turkish dataset of 400 sentences that are obtained from the METU-Sabanci Turkish Treebank. We provide the UCCA annotation specifications defined for the Turkish language so that it can be extended further. We followed a semi-automatic annotation approach, where an external semantic parser is utilised for the initial annotation of the dataset, which is manually revised by two annotators. We used the same semantic parser model to evaluate the dataset with zero-shot and few-shot learning, demonstrating that even a small sample set from the target language in the training data has a notable impact on the performance of the parser (15.6% and 2.5% gain over zero-shot for labelled and unlabelled results, respectively).